Scale Economies in European Trade · Crucially, scale economies are identi ed in relative terms...

Scale Economies in European Trade

Laura Bonacorsi

FEEM & CMCC

July 6th, 2017

Acknowledgement: This project has received funding from the EuropeanUnion’s Horizon 2020 research and innovation programme under grant agree-ment No 730403.

Laura Bonacorsi Scale Economies in European Trade July 6th , 2017 1 / 25

Introduction

Gravity models are one of the most successful framework for analyzinginternational trade flows.

They assume that bilateral trade flows are directly related to the size of origin anddestination and inversely related to their distance (a proxy for trade costs).

They have been widely used for policy purposes, such as analyzing the effects ofcommon currencies [Rose (2000)] or trade agreements [see Cipollina and Salvatici(2010) for a review] on trade flows.


Trade costs in Gravity Models

In gravity models, trade frictions come from the existence of region-pair specific“iceberg” trade cost: a fraction of every shipment melts during its transportation.

⇒ in order for 1 unit of goods or services to reach destination j from origin i ,ti,j > 1 units need to be shipped.

Trade costs are usually assumed to be constant between an origin and adestination: ti,j is independent from the volume of goods and services that areactually traded.

Anderson, Vesselovsky and Yotov (2016) are the first to depart from thisassumption: they allow for economies of scale in trade flows and show that thedata support this hypothesis (US-Canada trade).


My paper

In this paper, I will show that economies of scale in trade costs are strong inEurope as well.

Moreover, I will answer to the following questions:

Have the EU expansion played a role for the estimated scale elasticities?

Can I identify the determinants of scale economies in trade costs?


Preview of the results

Have the EU expansion played a role for the estimated scale elasticities?

On average, no. However, there is cross-sectoral heterogeneity.

Can I identify the determinants of scale economies in trade costs?

None of the product-level characteristics considered seems to play a role.

Country-level characteristics: the gain from additional volume doubles when exporting to themost corrupted countries.


Relationship to the Literature

This paper is related to

the gravity literature [see Head and Mayer (2014) for a review]. In particular, Ifollow AYV (2016) and show that scale economies in trade costs are an empiricalregularity in Europe as well

studies on the effects of the EU [Beltramo (2010), Chen (2004), Nitsch (2000)] andof the Euro [Glick and Rose (2001), Frankel and Rose (2002)] on internationaltrade flows

analysis of the impact of institutions on international trade [Anderson andMarcoullier (2002), Dutt and Traca (2010), Thede and Gustafson (2012)]


Theoretical Framework

AYV (2016) develop three main equations:

1 a microfounded gravity equation for bilateral trade flow Xi,j,t

2 a specification where trade frictions are allowed to be a function of tradevolumes Vi,j,t , according to a an elasticity φ, and also including the usualiceberg component τi,j

ti,j,t = τi,j(ri,trj,t

)ρjVφi,j

i,j,t

3 the definition of trade volumes

Vi,j,t =Xi,j,t

ti,j,t

ri,trj,t

ri,t and rj,t represent the appreciation of currencies i and j with respect to a baseperiod.


Theoretical Framework

The main parameter of interest is the scale elasticity φi,j :

φi,j =∂ti,j,t∂Vi,j,t

Vi,j,t

ti,j,t

Crucially, scale economies are identified in relative terms with respect to internal ones.

In fact, it is assumed that

φi,j = Bi,jφ =

{φ if Bi,j = 1 (i and j are two separate countries)0 if Bi,j = 0 (in case of internal trade)

φ represents the scale elasticity (what I will be testing for):- if φ > 0, trade costs are increasing in trade volumes (D.R.S)- if φ < 0, trade costs are decreasing in trade volumes (I.R.S)- if φ = 0, trade costs are constant (i.e. the model nests the traditional one)


Scale elasticity

φ can be computed from the estimated structural coefficients of the gravityspecification obtained from the three main equations:

Xi,j,t = exp[α0 + α1INTERNAL DISTi,i + α2INTERNAT DISTi,j+

δCONTIGUITYi,j + ζEXCH RATEi,j,t + βBORDERBi,j + θj,t + ηi,t ] + εi,j,t

See AYV’s gravity equation

In fact

α1 = γ1(1− σ)

α2 =γ1(1− σ)

1 + σφ

⇒ φ =1

σ(α1

α2− 1)


The Data

I constructed a comprehensive dataset for European bilateral flows and productionfigures (manufacturing) for the period 1980-2013 merging different sources :

What about the Euro?

Trade flows:

TradeProd: bilateral annual trade and production data for 26 industrialsectors (ISIC2 - 3digits) provided by CEPII- used for the period 1980 to 1995

Eurostat databases for trade (Comext) and production (Prodcom): availableat the product level - used for the period 1995 to 2013

More Info on Dataset Creation

Distances are population-weighted and follow the CEPII notes by Mayer andZignago (2006)

Exchange rate data: World Bank website (annual frequency)


The estimated φs

Assuming σ = 6.13 and using the following formula φ̂ = 1σ ( α̂1

α̂2− 1) (PPML

estimator by Santos-Silva and Tenreyro (2006))Sector φ S.E.Aggregate -0.073*** (0.004)Food Products -0.102*** (0.006)Beverages -0.01* (0.006)Tobaccoa -0.195*** (0.012)Textiles -0.045*** (0.008)Wearing apparela -0.167*** (0.008)Leatherpr -0.034*** (0.006)Footwear -0.059*** (0.008)WoodProd. -0.108*** (0.005)Furnit. -0.084*** (0.004)Paper&prod -0.081*** (0.005)Print&publ. -0.101*** (0.006)Ind.chem. -0.069*** (0.007)OtherChem.a -0.102*** (0.003)

Sector φ S.E.Petrol.ref.a -0.156*** (0.007)RubberProd. -0.03*** (0.006)PlasticProd. -0.044*** (0.01)Pottery -0.017*** (0.006)Glass&prod. -0.018*** (0.005)Non-metal.min.prod. -0.03*** (0.007)Iron&steel -0.057*** (0.005)Non-ferrMet -0.06*** (0.008)FabricMetPr -0.038*** (0.008)Machin -0.017*** (0.005)Machin,Electric -0.055*** (0.005)TransEquip -0.014** (0.007)ProfessEquip -0.162*** (0.017)

The average φ̂ is -0.073: a 10% increase in trade volumes corresponds to a 0.73%decrease in trade costs.a possible mis-specification of the trade cost function, as suggested by the INTERNAL DIST coefficient being positive

Gravity coefficientsLaura Bonacorsi Scale Economies in European Trade July 6th , 2017 11 / 25

A simple trade cost function

My results show that per-unit trade costs t are decreasing in trade volumes, as thefollowing trade cost function would imply

t =F

v+ c

where F represents fixed trade costs (supported by micro-evidence, see Robertsand Tybout (1997)) and c represents variable trade costs.

Hence, the scale elasticity becomes

φ =∂t

∂v

v

t= − F

F + vc

if F is positive, φ will be negative.

The absolute value of φ is increasing in F and decreasing in v

∂φ

∂F= − vc

(F + vc)2

∂φ

∂v=

cF

(F + vc)2


Uniformity

So far, I assumed uniform scale coefficients, i.e. scale elasticities were allowed tovary only across sectors but were assumed to be the same for all country-pairs.

What if I depart from this assumption?

Different dimensions can be considered:

EU vs non-EU members

Eurozone vs non-Eurozone members Go

large vs small countries Go


EU Membership

There could be differences in the scale elasticities implied by the expansion of theEU: EU members share a common set of rules and practices. Fixed trade costsshould be lower when trading with a fellow EU member (at least theirregulatory/institutional component) and/or trade volumes could be higher

→ φ closer to zero for EU trade

Name Accession Name AccessionBelgium Founder Sweden 1-Jan-95France Founder Cyprus 1-May-04Germany Founder Czech Rep. 1-May-04Italy Founder Estonia 1-May-04Luxembourg Founder Hungary 1-May-04Netherlands Founder Latvia 1-May-04Denmark 1-Jan-73 Lithuania 1-May-04Ireland 1-Jan-73 Malta 1-May-04UK 1-Jan-73 Poland 1-May-04Greece 1-Jan-81 Slovakia 1-May-04Portugal 1-Jan-86 Slovenia 1-May-04Spain 1-Jan-86 Bulgaria 1-Jan-07Austria 1-Jan-95 Romania 1-Jan-07Finland 1-Jan-95 Croatia 1-Jul-13

Go to full specification


EU elasticities: an example


Sector φ1 φ2Aggregate -0.083*** 0.002

(0.005) (0.003)Food Products -0.126*** 0.003

(0.006) (0.001)Beverages -0.02*** -0.036***

(0.007) (0.005)Tobacco -0.227*** -0.793

(0.016) (0.834)Textiles -0.064*** -0.016***

(0.009) (0.005)Wearing apparel -0.176*** -0.038**

(0.009) (0.015)Leatherpr -0.037*** -0.004

(0.006) (0.004)Footwear -0.104*** 0.001

(0.008) (0.003)WoodProd. -0.097*** 0.007

(0.005) (0.002)Furnit. -0.085*** 0.022

(0.005) (0.002)Paper&prod -0.082*** 0.006

(0.005) (0.002)Print&publ. -0.111*** 0.004

(0.007) (0.002)Ind.chem. -0.085*** 0.015

(0.008) (0.003)

Sector φ1 φ2OtherChem. -0.108*** 0.023

(0.003) (0.001)Petrol.ref. -0.13*** 0.019

(0.007) (0.01)RubberProd. -0.035*** -0.01*

(0.007) (0.006)PlasticProd. -0.039*** -0.017**

(0.012) (0.008)Pottery -0.031*** -0.018***

(0.006) (0.005)Glass&prod. -0.021*** -0.01**

(0.006) (0.005)Non-metal.min.prod. -0.037*** -0.012**

(0.007) (0.005)Iron&steel -0.075*** 0.002

(0.006) (0.003)Non-ferrMet -0.085*** 0.01

(0.01) (0.004)FabricMetPr -0.034*** -0.013**

(0.009) (0.006)Machin -0.004 -0.035***

(0.007) (0.005)Machin,Electric -0.055*** -0.015***

(0.006) (0.004)TransEquip -0.012 -0.036***

(0.009) (0.007)ProfessEquip -0.174*** -0.076

(0.017) (0.075)

Aggregate trade: scale elasticities when crossing the EU border are not stronger.For 11/26 sectors: the gain from additional volume is about 50% higher on average. All thesesectors exhibit high average levels of the weight-to-value ratio, proxy for shipping costs (seeHummels, 2007).

Go to extra-Europe


Possible factors affecting Scale Elasticities

Scale elasticities arise because of fixed trade costs. We could expect them to differaccording to

product-specific characteristics (product homogeneity, technical barriers totrade...) Go

country-specific characteristics (institutional variables)

My results show that the only the latter seem to play a role.


Corruption

The level of corruption has been proven to affect bilateral trade flows (seeAnderson and Marcoullier (2002), Dutt and Traca (2010)).

I will test whether shipping goods to more “corrupted” destinations affected thescale elasticities using the following equation:

X ki,j,t = exp[α0 + α1INTERNAL DISTi,i + α2INTERNAT DISTi,j+

α3INTERNAT DISTi,j × CORRUPj,t + γCORRUPj,t + δCONTIGUITYi,j+

ζEXCH RATEi,j,t + βBORDERBi,j + θj,t + ηi,t ] + εi,j,k,t

where CORRUPj,t is the control of corruption index from the WGI indicators,ranging from approximately -2.5 (weak) to 2.5 (strong) governance performance.

The most corrupted countries in the sample are Croatia and Latvia in 1996 (-0.642), whereas theleast ones are Finland in 2000 and Denmark in 2006 (2.5).


Corruption - Results

Internal dist -1.164***(-16.81)

Internat dist -1.532***(-32.24)

Corrupj,t× Internat dist 0.0826***(-17.69)

N 2796R2 0.99

The interaction with international distance is positive and significant (0.0826***):corruption depresses more trade on longer distances → scale elasticities are higher(in absolute value) the higher the importer’s level of corruption.

Example: exporting to Romania in 2000 (Corrup= -0.477, highest in the regression subsample)entails a scale elasticity of 0.38%, whereas exporting to Denmark in 2006 (Corrup=2.5) impliesmore than half the gain in terms of trade costs reduction: 0.16%.

Corruption and EU membership


What about environmental regulations?

Many papers study the effects that environmental regulations may have on tradeflows (see Cole and Elliott, 2003 and Jug and Mirza, 2005 among many others).If more stringent regulation affects the extensive margin (fixed cost) of tradeand/or trade volumes, it could be affecting scale elasticities too.

How to measure environmental regulation in a cross-country study?

EPS index by Botta and Kozluk (2014)

In order to use information for all countries in EPS database, I now use WIODdata (2013 release) on 14 manufacturing sectors - internal and international tradefigures already available.


Results

At the bilateral level (collapsing the sectoral dimension), the higher the EPS of theimporter the lower the scale elasticities, counterintuitive:

Internal dist -0.781***(-32.12)

Internat dist -0.849***(-32.24)

EPSj,t× Internat dist 0.018***

(-11.09)

N 13,108R2 0.99

But EPS is correlated with the institutional level:


Including “control of corruption” among the regressors the puzzling result(positive interaction) persists, even though economically small.

Internal dist -0.803***(-32.99)

Internat dist -0.859***(-53.82)


(2.82)

Corrj,t× Internat dist 0.033***

(-14.10)

N 10,672R2 0.99


What if I pool all sectors and interact with a sector-specific pre-sample EnvironmentalDependence (ED) variable (similarly to Albrizio et. al (2014)1.)

Internal dist -0.855***(-37.41)

Internat dist -1.076***(-79.88)


(16.64)

Corrj,t× Internat dist 0.071***

(-12.35)

EPSj,t× Internat dist × ED -0.005***

(-14.59)

Corrj,t× Internat dist × ED 0.000

(0.89)

N 149,279R2 0.99

Does this mean that environmental policy does not matter? No, it means that it doesnot matter for the distance elasticity of trade (whereas I proved that other institutionalvariables do).

1I constructed the ED ranking according to each sector’s CO2 emissions intensity per unit ofvalue added using WIOD I/O and WIOD Environmental data in 1995 for a benchmark country,USA


Conclusions

In this paper, I show that

trade costs are a decreasing function of trade volumes in bilateral-sectoralEuropean trade: on average, an increase in volumes by 10% is associatedwith a decrease in costs by 0.73%

the estimated scale elasticities are not influenced by the EU expansion onaverage. However, for some sectors they are 50% higher when trading with anon-EU members (consistent with having higher fixed costs and/or lowervolumes)

scale elasticities do not systematically vary according to differentproduct-level characteristics that I considered...

...but vary instead according to country-level institutional variables such asthe level of corruption


Moving forward

estimate sector specific CES elasticities

exploit data on transportation modes

compare manufacturing and services (WIOD)


Appendix


Anderson and van Wincoop (2003) show that the trade flow between i and j insector k (X k

i,j) can be expressed as

X ki,j = Y kski b

kj (

tki,jΠk

i Pkj

)1−σk

where

- Y k is the total of world shipments

- ski is the share of world shipment coming from origin i (ski,t =Y ki

Y k )

- bkj is the share of world shipment arriving to destination j from all possible

origins (bkj =E kj

Y k )

- tki,j represents the bilateral iceberg trade cost: for each unit shipped, only 1tki,j

reaches the destination- Πk

i and Pkj represent respectively the outward and the inward multilateral

resistance terms

Back to Theoretical Specification


Estimation allowing for economies (diseconomies) of scale

Using the expressions for Xi,j,t , Vi,j and ti,j is possible to write the followinggravity equation:

Xi,j,t = (cxi,tmj,t)1+φi,j

1+σφi,j (τi,j)1−σ

1+σφi,j (ri,trj,t

)(ρ−φi,j )(1−σ)

1+σφi,j

which can be taken to the data as follows:



where the coefficients depend on the structural parameters of the model.In particular,

α1 = γ1(1− σ) α2 =γ1(1− σ)

1 + σφ

Back to Theoretical Specification


Economies of scale in trade costs

Why could there be economies (diseconomies) of scale in trade costs?

- φ > 0 (trade costs increasing in trade volumes) ⇒ congestion story

Assume there is only one port, the increase in trade volume increases the tradefriction simply because it takes more time for the shipment to arrive to destination[Anderson and Bandiera (2006)]

- φ < 0 (trade costs decreasing in trade volumes) ⇒ fixed cost story

There may be fixed trade costs, whose unitary impact gets lower the higher theamount of goods shipped [Melitz (2003), Chaney (2008, 2014), Arkolakis (2010)]

Back to Estimating Equation


Results



(1) (2) (3) (4) (5) (6) (7)Food Products Beverages Tobacco Textiles Wearing apparel Leather pr est7

Internal dist -0.476∗∗∗ -1.538∗∗∗ 0.829∗∗∗ -0.823∗∗∗ 0.237∗∗∗ -1.126∗∗∗ -0.928∗∗∗(-6.37) (-18.54) (4.14) (-10.10) (3.03) (-15.23) (-10.99)

International Dist -1.674∗∗∗ -1.661∗∗∗ -2.244∗∗∗ -1.206∗∗∗ -1.371∗∗∗ -1.473∗∗∗ -1.577∗∗∗(-32.50) (-23.82) (-16.79) (-23.83) (-23.07) (-25.92) (-24.38)

Border 4.290∗∗∗ -1.562∗∗∗ 14.09∗∗∗ 0.328 7.424∗∗∗ 0.682∗∗ 1.529∗∗(10.31) (-3.95) (9.07) (0.90) (14.26) (1.98) (2.51)

Contig 0.310∗∗∗ -0.242∗∗∗ -0.243∗∗ 0.258∗∗∗ 0.256∗∗∗ 0.0386 0.214∗∗∗(5.70) (-3.36) (-1.99) (3.99) (3.85) (0.65) (4.14)

N 9548 8650 5262 9692 9451 8824 7762R2 1.0e+00 1.0e+00 1.0e+00 9.7e-01 9.6e-01 9.9e-01 9.9e-01

t statistics in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01


Results (2)

(1) (2) (3) (4) (5) (6)Footwear Wood prod. Furnit. Paper&prod Print&publ. Ind.chem.

Internal dist -0.361∗∗∗ -0.813∗∗∗ -0.595∗∗∗ -0.483∗∗∗ -0.862∗∗∗ -0.538∗∗∗(-6.05) (-11.56) (-10.62) (-5.98) (-9.74) (-11.52)

International Dist -1.492∗∗∗ -1.988∗∗∗ -1.375∗∗∗ -1.689∗∗∗ -1.677∗∗∗ -1.928∗∗∗(-33.81) (-31.82) (-36.77) (-25.61) (-42.39) (-55.28)

Exch. rate 0.236 0.454 0.341 0.0942 -0.251 -0.00506(0.00) (0.00) (0.00) (0.00) (-0.00) (-0.00)

Border 3.823∗∗∗ 4.957∗∗∗ 2.507∗∗∗ 3.844∗∗∗ 4.042∗∗∗ 6.477∗∗∗(13.62) (12.64) (8.73) (8.98) (8.08) (22.10)

Contig 0.602∗∗∗ 0.194∗∗∗ 0.365∗∗∗ 0.485∗∗∗ 0.122∗∗∗ -0.192∗∗∗(10.59) (3.24) (8.24) (6.71) (2.84) (-4.85)

N 8917 8724 9019 9013 9502 9209R2 9.9e-01 1.0e+00 9.9e-01 1.0e+00 9.6e-01 1.0e+00



Results (3)

(1) (2) (3) (4) (5) (6)Other chem. Rubber prod. Petrol.ref. Plastic prod. Pottery Glass & prod.

Internal dist 0.289∗∗∗ -1.115∗∗∗ -0.937∗∗∗ -1.493∗∗∗ -1.559∗∗∗ -1.462∗∗∗(2.89) (-17.54) (-9.15) (-16.17) (-22.01) (-13.98)

International Dist -2.113∗∗∗ -1.407∗∗∗ -1.350∗∗∗ -1.692∗∗∗ -1.789∗∗∗ -1.832∗∗∗(-25.19) (-22.85) (-20.46) (-21.59) (-35.01) (-26.76)

Exch. rate 0.524 -0.194 0.0386 -0.0654 0.122 -0.0273(0.00) (-0.00) (0.00) (-0.00) (0.00) (-0.00)

Border 11.26∗∗∗ 0.234 0.453 -0.669 0.101 0.113(20.83) (0.57) (0.80) (-1.49) (0.25) (0.24)

Contig -0.0507 0.190∗∗∗ 0.399∗∗∗ 0.157∗∗ 0.198∗∗∗ 0.169∗∗(-0.74) (3.48) (4.99) (2.09) (3.76) (1.99)

N 4409 9113 9196 8039 8727 9000R2 1.0e+00 9.8e-01 9.7e-01 1.0e+00 9.8e-01 9.9e-01



Results (4)

(1) (2) (3) (4) (5) (6) (7)Iron & steel Non-ferr met Fabric met pr Machin Machin, electric Trans Equip Profess Equip

Internal dist -0.854∗∗∗ -0.927∗∗∗ -1.166∗∗∗ -1.197∗∗∗ -0.778∗∗∗ -1.314∗∗∗ 0.208(-14.17) (-9.30) (-13.02) (-20.86) (-15.84) (-17.00) (1.12)

International Dist -1.415∗∗∗ -1.608∗∗∗ -1.584∗∗∗ -1.357∗∗∗ -1.272∗∗∗ -1.459∗∗∗ -1.529∗∗∗(-29.31) (-33.32) (-29.08) (-39.25) (-30.71) (-28.95) (-22.82)

Exch. rate 0.0614 0.0285 -0.00124 -0.170 -0.0904 -0.300 -0.00257(0.00) (0.00) (-0.00) (-0.00) (-0.00) (-0.00) (-0.00)

Border 1.173∗∗∗ 2.719∗∗∗ 0.693 -0.244 1.023∗∗∗ -0.208 9.843∗∗∗(3.55) (4.95) (1.33) (-0.85) (3.64) (-0.52) (8.53)

Contig 0.301∗∗∗ 0.193∗∗∗ 0.376∗∗∗ -0.00847 0.0434 0.219∗∗∗ -0.0494(6.16) (3.61) (4.73) (-0.23) (1.00) (4.61) (-0.82)

N 7940 8364 9533 9749 9631 9264 9079R2 1.0e+00 9.7e-01 9.7e-01 9.9e-01 9.9e-01 9.8e-01 8.6e-01


Back


Large vs Small countries

I consider country size with respect to either their GDP or their population (beingtime-varying, I ranked destination countries according to their average over thesample-period for these variables).

The model will be modified as follows

X ki,j,t = exp[α0 + α1INTERNAL DISTi,i + α2INTERNAT DIST largei,j+

α3INTERNAT DIST SMALLi,j + δ1CONTIG largei,j + δ2CONTIG SMALLi,j+

ζ1EXCH RATE largei,j,t + ζ2EXCH RATE SMALLi,j,t + βBORDERBB largei,j

βBORDERSB SMALLi,j + θj,t + ηi,t ] + εi,j,t

and therefore I will be able to back out the following parameters

φlarge =1

σ(α1

α2− 1) φSMALL =

1

σ(α1

α3− 1)


Large vs Small countries

Country size could imply differences in the scale elasticities: larger destinationcountries should exhibit lower scale elasticities (in absolute value) because:

trade volumes v are larger towards larger destinations → scale elasticitycloser to zero

In search-models a’ Chaney (2014), more likely to find a buyer in larger countries. Thecreation of contacts involves only the extensive margin of trade

in my setting, F lower towards larger destination markets → scale elasticitycloser to zero


Large vs Small countries - Results

Criterion φLARGE φSMALL

Population -0.075*** -0.090***(0.006) (0.005)

GDP -0.086*** -0.095***(0.005) (0.004)

N 13029 13017R2 0.99 0.99

As expected, the coefficients are closer to zero for larger destinations.

Sector by sector, I do not find significant differences, so I decided to keepuniformity for the remainder of the paper.

Back to Uniformity


I consider a different expression for trade costs:

ti,j,t = τi,j(ri,trj,t

)ρjV φi,j,t

The scale elasticity coefficient is now time varying: φi,j,t equals

0 if Bi,j = 0 (internal trade)φ1 if i and j are both in the EU at time t

φ1 + φ2 if i and j are not both in the EU at time t

It is possible to back out the scale elasticities φ1 and φ2 using the expressions for theestimated coefficients.


φi,j,t = Bi,j [φ1 + φ2Ui,j,t ]

where Ui,j,t takes value 1 if i and j are separated by a non-EU border at time t, i.e. if atleast one of the two is not a member of the European Union at time t.

X ki,j,t = exp[α0 + α1INTERNAL DISTi,i + α2INTERNAT DIST EUi,j,t+

α3INTERNAT DIST NONEUi,j,t + δ1CONTIGUITY EUi,j+

δ2CONTIGUITY NONEUi,j + ζ1EXCH RATE EUi,j,t+

ζ2EXCH RATE NONEUi,j,t + βBORDERBi,j (Ui,j,t = 0)

+ βBORDERNONEUBi,j (Ui,j,t = 1) + θj,t + ηi,t ] + εi,j,t

where:

α1 = γ1(1− σ) α2 =γ1(1− σ)

1 + σφ1α3 =

γ1(1− σ)

1 + σ(φ1 + φ2)

If α2 and α3 are statistically different⇒ φ2 is statistically different from zero⇒ economies of scale are different when at least one of the trade partners is not a member ofthe EU.

In particular, we will have that φ2 < 0 if |α2| < |α3|.

Back to EU elasticities


Product Homogeneity

Proximity and cultural links affect bilateral trade to an higher extent fordifferentiated goods as opposed to homogeneous goods, whose quality andcharacteristics are more subject to asymmetric information issues (Rauch 1999).

This could be in place for scale elasticities, too. I checked for this hypothesis bypooling the data (at the product-level, from 1996 onwards) and estimating thefollowing:

X ki,j,k,t = exp[α0 + α1INTERNAL DISTi,i + α2INTERNAT DISTi,j+

α3(INTERNAT DISTi,j × DEGREE HOMOGk )+

δCONTIGUITYi,j + ζEXCH RATEi,j,t + βBORDERBi,j + θj,t + ηi,t ] + εi,j,k,t

The data do not support my hypothesis. The relevance of other product-levelvariables (weight to value ratio, technical barriers to trade) was also rejected bythe data using the same methodology.

Back


What about the Euro?

In the baseline, I estimate scale elasticities over the 1980-2013 period independently fromthe introduction of the Euro.

Frankel and Rose (2002) among many others show that the introduction of a commoncurrency has significant effects on trade and income. This could alter the meaning of myestimates.

However,

analyzing the whole sample allows me to consider the full EU expansion, up untilthe last accession of Croatia in 2013

including the whole sample in the regressions does not alter the magnitude of theresults (S.E. estimated over the 1980-2001 period are not significantly differentthan the one estimated over the whole sample)

Eurozone specific S.E. are, on average, 0.94% when exporting to a Eurozonemember and 0.79% when exporting to a EU member not in the Eurozone. Sectorby sector, differences are not economically significant and have opposite signs.

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Extra European countries

Sector χ1 χ2Aggregate -0.079*** -0.021***

(0.005) (0.004)Food Products -0.11*** -0.003

(0.006) (0.002)Tobacco -0.193*** -0.098*

(0.012) (0.055)Textiles -0.044*** 0.029

(0.007) (0.016)Wearing apparel -0.146*** -0.004

(0.007) (0.009)Leatherpr -0.024*** 21.254

(0.005) (434.768)Footwear -0.028*** 0.217

(0.009) (0.239)WoodProd. -0.103*** 0.022

(0.004) (0.009)Furnit. -0.084*** 0.032

(0.004) (0.015)Paper&prod -0.083*** 0.044

(0.005) (0.015)Print&publ. -0.109*** -0.009***

(0.005) (0.003)Ind.chem. -0.077*** -0.018***

(0.008) (0.003)

Sector χ1 χ2OtherChem. -0.106*** -0.005**

(0.003) (0.002)Petrol.ref. -0.157*** 0.003

(0.007) (0.002)RubberProd. -0.041*** 0.081

(0.007) (0.048)PlasticProd. -0.055*** -0.016

(0.011) (0.011)Pottery -0.018*** -0.039***

(0.006) (0.009)Glass&prod. -0.024*** -0.011

(0.005) (0.012)Non-metal.min.prod. -0.026*** 0.109

(0.006) (0.052)Iron&steel -0.06*** -0.015**

(0.005) (0.007)FabricMetPr -0.041*** -0.021***

(0.008) (0.007)Machin -0.02*** -0.051***

(0.006) (0.005)Machin,Electric -0.064*** -0.04***

(0.005) (0.004)TransEquip -0.024*** -0.066***

(0.007) (0.006)ProfessEquip -0.22*** 0.03

(0.027) (0.012)

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Notes on Dataset Creation

Tradeprod: ISIC2, 3digitsProdcom: Nace Rev.2 classificationComext: CN code → converted to CPA code using the RAMON Tables → NaceRev.2 classification

I created a conversion table linking Nace Rev.2 to ISIC2, 3digits using the UnitedNations Statistics Division tables as follows:

Nace Rev.2 → Isic Rev.4 → Isic Rev. 3.1 → ISIC2, 3digits

When two entries were included in Comext data, I kept the importers figure.

TradeProd data is in thousands of dollars. Prodcom data (in thousands of ECU)and Comext data (in Euro) were converted in dollars using the currencyconversion tables provided by the Eurostat and the OANDA database(oanda.com) on historical currency rates.

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Corruption and EU membership

(1) (2)Internal dist -1.164*** -1.141***

(-16.81) (-16.26)

Internat dist -1.532*** -1.515***(-32.24) (-31.42)

Corruptionj,t× Internat dist 0.0826***(-17.69)

Corruptionj,t× Internat dist × BothEU 0.0819***(17.59)

Corrupj,t× Internat dist × (1-BothEU) 0.0616***(8.05)

N 2796 2796R2 0.99 0.99

Corruption matters independently on the EU membership of the trade partners(Column (2)). The estimates of the interaction coefficients are statisticallydifferent when both countries are EU members (BothEU = 1) or not(BothEU = 0), but the estimated φ are economically the same.

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